Gradual Study Advising with Course Knowledge Graphs

Junnan Dong, Wentao Li, Yaowei Wang, Qing Li, George Baciu, Jiannong Cao, Xiao Huang, Richard Chen Li, Peter H.F. Ng

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review


Knowledge graphs (KGs) have been actively studied for pedagogical purposes. To depict the rich but latent relations among different concepts in the course textbook, increasing efforts have been proposed to construct course KGs for university students. However, the application of course KGs for real study scenarios and career development remains unexplored and nontrivial. First, it is hard to enable personalized viewing and advising. Within the intricate university curricula, instructors aim to assist students in developing a personalized course selection pathway, which cannot be fulfilled by isolated course KGs. Second, locating concepts that are important to individuals poses challenges to students. Real-world course KGs may contain hundreds of concepts connected by hierarchical relations, e.g., contain_subtopic, making it challenging to capture the key points. To tackle these challenges, in this paper, we present GSA, a novel gradual study advising system based on course knowledge graphs, to facilitate both intra-course study and inter-course development for students significantly. Specifically, (i) we establish an interactive web system for both instructors to construct and manipulate course KGs, and students to view and interact. (ii) Concept-level advising is designed to visualize the centrality of a course KG based on various metrics. We also propose a tailored algorithm to suggest the learning path based on what concepts students have learned. (iii) Course-level advising is instantiated with a course network. This indicates the prerequisite relation among different levels of courses, corresponding to the annually increasing curricular design and forming different major streams. Extensive illustrations show the effectiveness of our system.

Original languageEnglish
Title of host publicationAdvances in Web-Based Learning – ICWL 2023 - 22nd International Conference, ICWL 2023, Proceedings
EditorsHaoran Xie, Chiu-Lin Lai, Wei Chen, Guandong Xu, Elvira Popescu
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages14
ISBN (Print)9789819983841
Publication statusPublished - 2023
Event22nd International Conference on Web-based Learning, ICWL 2023 - Sydney, Australia
Duration: 26 Nov 202328 Nov 2023

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume14409 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference22nd International Conference on Web-based Learning, ICWL 2023


  • Graph Visualization
  • Knowledge Graphs
  • Study Advising

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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